CN110557799B - Data acquisition method based on mobile vehicle in smart city edge network - Google Patents
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Abstract
The invention discloses a data acquisition method based on a mobile vehicle in a smart city edge network, which comprises the following steps: the sensor node acquires the hop count of the vehicle; evaluating the transmission cost from the alternative cluster head to the vehicle according to the transmission distance from the alternative cluster head to the vehicle and the residual energy, and selecting the alternative cluster head with the minimum cost as a cluster head node; evaluating the transmission cost of the source node to the vehicle through each candidate father node according to the minimum node energy in the transmission distance and the path to the vehicle through each candidate father node, and selecting the candidate father node with the minimum cost as the father node; for the source nodes with hop number of 1 and non-cluster head, all taking the cluster head nodes as father nodes; each sensor node sends the data cached by the sensor node and the received data to the parent node, and finally sends the data to the vehicle through the cluster head. The invention considers the transmission distance and the remaining energy factors to select the cluster head and the father node, can avoid the phenomenon of energy holes caused by premature death of the cluster head and the father node, and effectively finishes data acquisition.
Description
Technical Field
The invention relates to the field of data acquisition, in particular to a data acquisition method based on a mobile vehicle in a smart city edge network.
Background
The smart city is a trend of city development in the future, the sensor can be embedded into the infrastructure of the city to sense the state of the smart city, and the smart city has wide application in the process of building the smart city. The thing networking utilizes sensing equipment to be connected to the infrastructure of different grade type to the internet for the dream in smart city gradually becomes reality. Through wide network interconnection, the sensing equipment can quickly acquire a large amount of data, and the processed data can provide scientific decision support for municipal departments, thereby being beneficial to promoting the intelligent management and development of cities. The realization of the good prospect in the smart city requires that sensing equipment can be deployed in the city rapidly and flexibly, and the data acquisition problem in the smart city is better solved. Therefore, the data collection problem in the smart city becomes a hot problem in the current research.
In a data acquisition method of a conventional Wireless Sensor Network (WSN), a connected network composed of a plurality of sensor nodes and a sink is generally studied. Data sensed by each sensor node can be routed to the sink in a multi-hop routing mode, and the sink is connected with the Internet, so that data collection is achieved. The perception network in the smart city is fundamentally different from the traditional WSN. In the perception network of the smart city, a large number of sensor nodes are deployed according to the needs of applications, and no underlying communication facility can be connected to the Internet in many applications, so that the network can be abstracted to form a plurality of distributed and unconnected isolated WSNs for a large number of sensors distributed in the smart city. Although the separated WSNs can be internally organized into a connected network, the connected network is probably just a common sensor node and does not have a sink connected to the Internet due to the limitation of application scenarios. For example, in a smart city, the perceptual network deployed as road construction sites advance, does not yet have the basic conditions for connecting to the Internet. On the other hand, deploying a proprietary network connected to the Internet also requires a significant construction cost. For many temporarily deployed perception networks in smart cities, it is neither timely nor economical to deploy networks specifically connected to the Internet.
At present, some data acquisition methods are provided for acquiring data through vehicles in a city, the city is provided with a large number of vehicles, the vehicles can cover all roads of a smart city when driving, and a transceiver is integrated in a chip of the vehicle and can exchange data with nearby sensors and data centers. In this case, the sensor nodes near the road may function as a sink. However, in the existing vehicle-based data collection method, only data collection of a single sensor node at the edge of a road is involved, and no sensor node far away from the road and incapable of communicating with a vehicle is involved, so that no method can be used for collecting data of all sensor nodes in a large number of separated WSNs deployed in a smart city. Therefore, it is necessary to provide a low-cost and efficient data acquisition method for the edge network in the smart city.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a low-cost method for effectively collecting data of all sensor nodes in a large number of separated WSNs deployed in a smart city.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
a data acquisition method based on moving vehicles in a smart city edge network is characterized in that 1 vehicle is configured for each separated wireless sensor network in the smart city edge network, the vehicles circularly run in the corresponding wireless sensor network and acquire data of each sensor node, wherein the data acquisition process of each cycle of the vehicles comprises the following steps:
step 1, broadcasting a message by a vehicle, and acquiring hop counts from the vehicle to each sensor node in a wireless sensor network and recording the hop counts as the hop counts of the sensor nodes;
step 2, updating the cluster head of the wireless sensor network;
selecting a sensor node with hop number h being 1 as an alternative cluster head of the wireless sensor network; for each alternative cluster head, evaluating the data transmission overhead from the alternative cluster head to the vehicle according to the data transmission distance from the alternative cluster head to the vehicle and the residual energy of the alternative cluster head; taking the alternative cluster head with the minimum data transmission overhead as a cluster head node;
step 3, determining father nodes of all sensor nodes;
for source nodes with hop count h being more than or equal to 2, selecting sensor nodes with hop count h-1 as 1 candidate father nodes of the source nodes; for each source node with the hop number h being more than or equal to 2, evaluating data transmission cost of the source node reaching the vehicle through each candidate father node respectively according to the data transmission distance of the source node reaching the vehicle through each candidate father node respectively and the minimum node energy in a path of the source node reaching the vehicle through each candidate father node respectively, and taking the candidate father node corresponding to the minimum value of the data transmission cost as the father node of the source node;
for the source nodes with hop number h equal to 1 and non-cluster-head nodes, taking the cluster-head nodes as the father nodes;
step 4, each sensor node sends data;
each sensor node sends the data cached by itself and the data received from the child node thereof to the father node, and finally sends the data to the vehicle through the cluster head node.
Further, for each candidate cluster head, the data transmission overhead from the candidate cluster head to the vehicle is evaluated according to the data transmission distance from the candidate cluster head to the vehicle and the remaining energy of the candidate cluster head, and the specific evaluation method is as follows:
costh(i)＝αwDh(i)+(1-α)wEh(i)；
in the formula, costh(i) Indicating alternative cluster head hiData transfer overhead to the vehicle, wDh(i) Indicating alternative cluster head hiA transmission distance factor of wEh(i) Indicating alternative cluster head hiα is a weight factor selected from cluster head, and has a value of 0 ≤ α ≤ 1, and D (h)iMF) represents an alternative cluster head hiDistance to vehicle MF, max (D (h)iMF)) represents the maximum of the distances of all candidate nodes to the vehicle; e (h)i) Indicating alternative cluster head hiResidual energy of (d), max (E (h)i) Represents all alternative cluster heads hiThe maximum of the remaining energy.
Further, for each source node with the hop count h being greater than or equal to 2, the data transmission overhead of the source node reaching the vehicle via each candidate father node is evaluated according to the data transmission distance of the source node reaching the vehicle via each candidate father node and the minimum node energy in the path of the source node reaching the vehicle via each candidate father node, and the specific evaluation method is as follows:
costn(j,k)＝βwDn(j,k)+(1-β)wEn(j,k)；
in the formula, njRepresenting a source node, nkRepresenting a source node njCandidate parent node of, costn(j, k) denotes a source node njVia candidate parent node nkData transmission overhead, w, to vehicle MFDn(j, k) denotes a source node njVia candidate parent node nkTransmission distance factor, w, to vehicle MFEn(i, k) denotes a source node njVia candidate parent node nkThe remaining energy factor to the vehicle MF, β, represents the parent node selection weight factor, and has a value of 0 ≦ β ≦ 1, D (n)j,nkMF) represents a source node njVia candidate parent node nkDistance to vehicle MF, max (D (n)j,nkMF)) as a source node njRespectively via each candidate parent node nkIs calculated to D (n)j,nkMaximum value in MF); emin(nj,nkMF) as a source node njVia candidate parent node nkMinimum node energy in the path to the vehicle MF, max (E)min(nj,nkMF)) as a source node njVia each candidate parent node nkIs calculated to obtain Emin(nj,nkMF).
Further, the source node njVia candidate parent node nkDistance D (n) to vehicle MFj,nkMF) calculation method:
D(nj,nk,MF)＝D(nj,nk)+D(nk,MF)；
in the formula, D (n)j,nk) Representing a source node njWith its candidate parent node nkThe distance between the two nodes is calculated according to the GPS information of the two nodes; d (n)kMF) represents a candidate parent node nkAnd between vehicles MFDistance according to candidate parent node nkAnd calculating the GPS information of the vehicle MF.
Further, the method for acquiring the hop count from each node to the vehicle in the wireless sensor network through the vehicle broadcast message includes:
step 1.1, a vehicle sends out a broadcast message in the process of traveling, wherein the broadcast message is a data packet which comprises hop count and the initial hop count is 0;
step 1.2, after receiving the broadcast message, the sensor node adds 1 to the hop count in the data packet, takes the obtained hop count as the hop count from the sensor node to the vehicle, and then continuously broadcasts the hop count from the sensor node to the sensor node in the communication range of the sensor node;
step 1.3, repeating step 1.2 until no new broadcast occurs in the current wireless sensor network;
and if the sensor node receives a plurality of data packets, taking the modified minimum value of the hop count in all the data packets as the hop count from the sensor node to the vehicle.
Advantageous effects
When the cluster head is selected, the two factors of the transmission distance from the alternative cluster head to the vehicle and the residual energy of the alternative cluster head are comprehensively considered, so that the weights of the two factors are set and adjusted according to actual requirements, the alternative cluster head with the minimum data transmission overhead is selected as the cluster head of the wireless sensor network of the current vehicle, and the condition that the energy consumption of the cluster head is overlarge due to the fact that a node with a larger transmission distance is selected as the cluster head can be avoided; similarly, when the parent node is selected, the factors of the transmission distance from each candidate parent node to the vehicle and the residual energy of the minimum energy node in the path are comprehensively considered, so that the weights of the two factors are set and adjusted according to actual requirements, and the candidate parent node with the minimum data transmission cost is selected as the parent node of the current source node, so that the condition that the minimum energy node in the path where the candidate parent node is located is prematurely dead due to the fact that a large path distance passing through the candidate parent node is selected can be avoided. Therefore, the invention can avoid the energy void phenomenon caused by the early death of the nodes in the wireless network, thereby effectively finishing the data acquisition.
In addition, the data are acquired through the vehicle circulation, the cluster heads are reset and the father nodes are reselected according to the states of the nodes in the wireless sensor network in each circulation, the problem that the data cannot be effectively acquired due to dead nodes can be further avoided, and the data acquisition can be effectively finished.
Drawings
FIG. 1 is a diagram of a smart city edge network according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method according to an embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in detail, which are developed based on the technical solutions of the present invention, and give detailed implementation manners and specific operation procedures to further explain the technical solutions of the present invention.
The invention provides a data acquisition method based on a mobile vehicle in a smart city edge network, which comprises the following steps that 1 vehicle is configured for each separated wireless sensor network in the smart city edge network, as shown in figure 1, the vehicles circularly run in the corresponding wireless sensor network and acquire data of each sensor node, and as shown in figure 2, the process of acquiring data by the vehicles in each cycle comprises the following steps:
step 1, broadcasting messages by vehicles, and recording hop counts from the sensors to the vehicles by the sensor nodes in the wireless sensor network as the hop counts of the sensor nodes.
Step 1.1, the vehicle travels once along the road, so as to obtain the hop count from the sensor node to the vehicle. The method comprises the steps that a vehicle sends out a broadcast message in the process of traveling, wherein the broadcast message is a data packet which comprises hop counts and the initial hop count is 0;
step 1.2, after receiving the broadcast message, the sensor nodes in the vehicle communication range add 1 to the hop count in the data packet, and the obtained hop count is used as the hop count from the sensor node to the vehicle, and then the hop count from the sensor node to the vehicle is continuously broadcast to the sensor nodes in the vehicle communication range;
and 1.3, repeating the step 1.2 until no new broadcast occurs in the current wireless sensor network.
The sensor node may receive a plurality of broadcast messages, that is, a plurality of data packets, and if the hop counts are different, the modified minimum hop count is used as the hop count from the sensor node to the vehicle. At the end, each sensor node knows its own hop count to the vehicle.
Step 2, setting a cluster head of the wireless sensor network;
selecting a sensor node with hop number h being 1 as an alternative cluster head of the wireless sensor network; for each alternative cluster head, evaluating the data transmission overhead from the alternative cluster head to the vehicle according to the data transmission distance from the alternative cluster head to the vehicle and the residual energy of the alternative cluster head; taking the alternative cluster head with the minimum data transmission overhead as a cluster head node; the method for evaluating the data transmission overhead from each candidate cluster head to the vehicle comprises the following steps:
costh(i)＝αwDh(i)+(1-α)wEh(i)；
in the formula, costh(i) Indicating alternative cluster head hiData transfer overhead to the vehicle, wDh(i) Indicating alternative cluster head hiA transmission distance factor of wEh(i) Indicating alternative cluster head hiα is a weight factor selected from cluster head, and has a value of 0 ≤ α ≤ 1, and D (h)iMF) represents an alternative cluster head hiDistance to vehicle MF, max (D (h)iMF)) represents the maximum of the distances of all candidate nodes to the vehicle; e (h)i) Indicating alternative cluster head hiResidual energy of (d), max (E (h)i) Represents all alternative cluster heads hiThe maximum of the remaining energy.
In the data transmission overhead evaluation from the alternative cluster head to the vehicle in the embodiment, two influencing factors are considered: transmission distance and remaining energy.
1) Transmission distance: the distance of the nodes from the vehicle has a large impact on the data transmission overhead, since the energy consumed by the sensor nodes is proportional to the data transmission distance. If the cluster head is too far away from the vehicle, the energy consumption of the cluster head is too large during data transmission. Therefore, the present embodiment introduces the distance from the node to the vehicle as an influence factor of the cost function, that is, the transmission distance from the candidate cluster head to the vehicle is used as an influence factor of the data transmission overhead.
When the cluster head h is selectediDistance D (h) to vehicle MFiThe smaller MF), the transmission distance factor wDh(i) Smaller, alternative cluster head hiData transfer overhead cost to vehicle MFh(i) The smaller the alternative cluster head hiThe higher the probability of being selected as a cluster head.
2) Residual energy: the remaining energy of the node also has a great influence on the life cycle of the network, if the remaining energy of the alternative cluster head is too small, the alternative cluster head is easy to die prematurely, and becomes a failure node, so that all data cannot be sent to the vehicle through the alternative cluster head. Therefore, the present embodiment introduces the node residual energy as an influence factor of the cost function, that is, the residual energy of the candidate cluster head is used as an influence factor of the data transmission overhead.
When the cluster head h is selectediResidual energy E (h)i) Larger, alternative cluster head hiIs the residual energy factor wEh(i) Larger, alternative cluster head hiData transfer overhead cost to vehicle MFh(i) The smaller the alternative cluster head hiThe higher the probability of being selected as a cluster head.
In addition, cluster head selection weight factor α is used to adjust the transmission distance factor wDh(i) And a residual energy factor wEh(i) If it is desired to select a node close to the vehicle as a cluster head, α is adjusted to be large, α being 1 means that only the transmission distance factor is considered, and α being adjusted to be small, if it is desired to select a node with large remaining energy as a cluster head, α being 0 means that only the energy factor is considered.
The embodiment partitions the city edge network into a large number of partitioned WSNs according to the roads. In each separated WSN, the vehicle runs for one circle in each cycle, and one round of cluster head rotation is carried out in the area, namely the separated WSN resets the cluster heads according to the data transmission cost of the current alternative cluster heads as the cluster heads, so that the premature death of the cluster heads can be avoided in the data acquisition process, the energy void phenomenon can be effectively avoided, and the energy consumption balance is realized.
Step 3, determining father nodes of all sensor nodes;
through the cluster head rotation strategy in the step 2, each vehicle cycle is reset with cluster heads, each separated WSN selects a cluster head, the cluster head can directly communicate with the vehicle, sensor nodes far away from the road side in the WSN need to transmit data to the cluster head through multi-hop communication, and then the cluster head transmits the data to the vehicle. And 3, designing a clustering algorithm, namely determining a father node of each sensor node, so that the WSNs separated in the smart city edge network can form a cluster, and the nodes in the cluster route data to a cluster head in a multi-hop communication mode and then transmit the data to the vehicle by the cluster head.
Each node within the WSN needs to select a parent node and transmit data to the vehicle via the parent node. In the process of parent node selection, a node with hop count h needs to send data to a node with hop count h-1, then send the data to a node with hop count h-2, and repeat the process until the data is sent to a cluster head with hop count 1. Specifically, the selection is made in two cases: for source nodes with the hop count h being more than or equal to 2, selecting sensor nodes with the hop count h-1 as 1 candidate father nodes of the source nodes, and then evaluating data transmission cost of the source nodes reaching vehicles through the candidate father nodes respectively so as to take the candidate father nodes corresponding to the minimum value of the data transmission cost as the father nodes of the source nodes; and for the source nodes with hop count h equal to 1 and not cluster head nodes, the cluster head node is taken as the father node.
Since there may be a plurality of sensor nodes with the same hop count, each source node may have a plurality of candidate father nodes, and this embodiment evaluates the data transmission overhead of the source node reaching the vehicle via each candidate father node according to the data transmission distance of the source node reaching the vehicle via each candidate father node and the minimum node energy in the path of the source node reaching the vehicle via each candidate father node, respectively, by designing a cost function, the specific evaluation method is as follows:
costn(j,k)＝βwDn(j,k)+(1-β)wEn(j,k)；
in the formula, njRepresenting a source node, nkRepresenting a source node njCandidate parent node of, costn(j, k) denotes a source node njVia candidate parent node nkData transmission overhead, w, to vehicle MFDn(i, k) represents the source node via candidate parent node nkTransmission distance factor, w, to vehicle MFEn(i, k) denotes a source node njVia candidate parent node nkThe remaining energy factor to the vehicle MF, β, represents the parent node selection weight factor, and has a value of 0 ≦ β ≦ 1, D (n)j,nkMF) represents a source node njVia candidate parent node nkDistance to vehicle MF, max (D (n)j,nkMF)) as a source node njRespectively via each candidate parent node nkIs calculated to D (n)j,nkMaximum value in MF); emin(nj,nkMF) as a source node njVia candidate parent node nkMinimum node energy in the path to the vehicle MF, max (E)min(nj,nkMF)) as a source node njVia each candidate parent node nkIs calculated to obtain Emin(nj,nkMF).
In this embodiment, the data transmission overhead of the source node arriving at the vehicle via each candidate parent node is evaluated, and two influencing factors are also considered: transmission distance and remaining energy.
1) Transmission distance: the distance of the source node to the vehicle via the candidate parent node has a large impact on the data transmission overhead because the energy consumed by the sensor node is proportional to the distance of the data transmission path. If the distance of the path from the source node to the vehicle via a candidate parent node is too far, the energy consumption of the source node may be excessive for data transmission through the path. The path distance of the source node to the vehicle via the candidate parent node is thus introduced as an influencing factor of the cost function.
2) Residual energy: in the path from the source node to the vehicle, the sensor node with the minimum residual energy on the path is the bottleneck of the whole path, so when the father node is selected, the node with the minimum energy in the path where each candidate father node is located is selected firstly, then the minimum energy node of each path is compared, the node with the maximum energy is selected from the nodes, and the candidate father node in the path where the node is located is the father node to be selected finally, so that the death caused by the overlarge energy consumption of the minimum energy node in the path can be avoided. Therefore, the present embodiment introduces the residual energy as an impact factor of the cost function.
For example, if the source node njReceiving node npAnd nqTransmitted signal, from n by needpAnd nqOne of which is selected as the parent node. Source node njRespectively calculating transmission data respectively via the nodes npAnd nqThe costs to the vehicle MF are cost (j, p) and cost (j, q), respectively, and if cost (j, p) ≦ cost (j, q), node njSelecting a node npAs a parent node, if cost (j, p)>cost (j, q) is node njSelecting a node nqAs a parent node.
In the parent node selection process, the source node needs to select a parent node from a plurality of candidate parent nodes. First, initialization is performed to set the maximum transmission distance max (D (n)j,nkMF)) and minimum node maximum remaining energy max (E)min(nj,nkMF)) is set to 0 and the minimum cost min (cost (j, k)) is set to ∞. For a source node njFrom a plurality of candidate nodes nkEach of the information received in (1), extracting therefromGo out nkGPS information GPSnk＝(xk,yk) Thereby calculating nkDistance to vehicle D (n)kMF), and calculating nkMinimum node energy E into MF pathmin(nkMF). Then calculate njTo nkDistance D (n)j,nk) And thus njVia nkDistance D (n) to MFj,nk,MF)＝D(nj,nk)+D(nkMF), and selects all nkMaximum value of distance max (D (n)j,nkMF)). Calculating njVia each nkRemaining energy E to minimum node in all paths of vehicle MFmin(nj,nkMF), and the maximum value max (E) of all energiesmin(nj,nkMF)). Next, a source node n is calculatedjVia candidate parent node nkTransmission distance factor w to vehicle MFDn(j, k) and source node njVia candidate parent node nkRemaining energy factor w to vehicle MFEn(i, k) and calculating the source node n according to a preset parent selection weight factor βjVia each candidate parent node nkThe data transmission cost (j, k) required for transmitting data to the vehicle, from which the minimum value min (cost (j, k)) is found, and finally the source node njSelecting node n in cost minimum min (cost (j, k))kAs its parent node. Finally, energy balance can be realized, premature death of nodes in a data transmission path is avoided, and an energy void phenomenon can be effectively avoided.
Step 4, each sensor node sends data;
each sensor node sends the data cached by itself and the data received from the child node thereof to the father node, and finally sends the data to the vehicle through the cluster head node.
The above embodiments are preferred embodiments of the present application, and those skilled in the art can make various changes or modifications without departing from the general concept of the present application, and such changes or modifications should fall within the scope of the claims of the present application.
Claims (4)
1. A data acquisition method based on moving vehicles in a smart city edge network is characterized in that 1 vehicle is configured for each separated wireless sensor network in the smart city edge network, the vehicles circularly run in the corresponding wireless sensor network and acquire data of each sensor node, wherein the data acquisition process of each cycle of the vehicles comprises the following steps:
step 1, broadcasting a message by a vehicle, and acquiring hop counts from the vehicle to each sensor node in a wireless sensor network and recording the hop counts as the hop counts of the sensor nodes;
step 2, updating the cluster head of the wireless sensor network;
selecting a sensor node with hop number h being 1 as an alternative cluster head of the wireless sensor network; for each alternative cluster head, evaluating the data transmission overhead from the alternative cluster head to the vehicle according to the data transmission distance from the alternative cluster head to the vehicle and the residual energy of the alternative cluster head; taking the alternative cluster head with the minimum data transmission overhead as a cluster head node;
step 3, determining father nodes of all sensor nodes;
for source nodes with hop count h being more than or equal to 2, selecting sensor nodes with hop count h-1 as 1 candidate father nodes of the source nodes; for each source node with the hop number h being more than or equal to 2, evaluating data transmission cost of the source node reaching the vehicle through each candidate father node respectively according to the data transmission distance of the source node reaching the vehicle through each candidate father node respectively and the minimum node energy in a path of the source node reaching the vehicle through each candidate father node respectively, and taking the candidate father node corresponding to the minimum value of the data transmission cost as the father node of the source node;
for the source nodes with hop number h equal to 1 and non-cluster-head nodes, taking the cluster-head nodes as the father nodes;
step 4, each sensor node sends data;
each sensor node sends the data cached by the sensor node and the data received from the child nodes to the father node and finally sends the data to the vehicle through the cluster head node;
for each alternative cluster head, estimating data transmission overhead from the cluster head to the vehicle according to the data transmission distance from the cluster head to the vehicle and the residual energy of the cluster head, wherein the specific estimation method comprises the following steps:
costh(i)＝αwDh(i)+(1-α)wEh(i)；
in the formula, costh(i) Indicating alternative cluster head hiData transfer overhead to the vehicle, wDh(i) Indicating alternative cluster head hiA transmission distance factor of wEh(i) Indicating alternative cluster head hiα is a weight factor selected from cluster head, and has a value of 0 ≤ α ≤ 1, and D (h)iMF) represents an alternative cluster head hiDistance to vehicle MF, max (D (h)iMF)) represents the maximum of the distances of all candidate nodes to the vehicle; e (h)i) Indicating alternative cluster head hiResidual energy of (d), max (E (h)i) Represents all alternative cluster heads hiThe maximum of the remaining energy.
2. The method according to claim 1, wherein for each source node with hop number h ≧ 2, the data transmission overhead of the source node to the vehicle via each candidate parent node is evaluated according to the data transmission distance of the source node to the vehicle via each candidate parent node respectively and the minimum node energy in the path of the source node to the vehicle via each candidate parent node respectively, and the specific evaluation method is as follows:
costn(j,k)＝βwDn(j,k)+(1-β)wEn(j,k)；
in the formula, njRepresenting a source node, nkRepresenting a source node njCandidate parent node of, costn(j, k) denotes a source node njVia candidate parent node nkData transmission overhead, w, to vehicle MFDn(j, k) denotes a source node njVia candidate parent node nkTransmission distance factor, w, to vehicle MFEn(i, k) denotes a source node njVia candidate parent node nkThe remaining energy factor to the vehicle MF, β, represents the parent node selection weight factor, and has a value of 0 ≦ β ≦ 1, D (n)j,nkMF) represents a source node njVia candidate parent node nkDistance to vehicle MF, max (D (n)j,nkMF)) as a source node njRespectively via each candidate parent node nkIs calculated to D (n)j,nkMaximum value in MF); emin(nj,nkMF) as a source node njVia candidate parent node nkMinimum node energy in the path to the vehicle MF, max (E)min(nj,nkMF)) as a source node njVia each candidate parent node nkIs calculated to obtain Emin(nj,nkMF).
3. The method of claim 1, wherein a source node njVia candidate parent node nkDistance D (n) to vehicle MFj,nkMF) calculation method:
D(nj,nk,MF)＝D(nj,nk)+D(nk,MF)；
in the formula, D (n)j,nk) Representing a source node njWith its candidate parent node nkThe distance between the two nodes is calculated according to the GPS information of the two nodes; d (n)kMF) represents a candidate parent node nkDistance from vehicle MF according to candidate parent node nkGPS information meter with vehicle MFAnd (5) calculating to obtain.
4. The method according to claim 1, wherein the hop count from each node to the vehicle in the wireless sensor network is obtained through vehicle broadcast messages, and the specific method is as follows:
step 1.1, a vehicle sends out a broadcast message in the process of traveling, wherein the broadcast message is a data packet which comprises hop count and the initial hop count is 0;
step 1.2, after receiving the broadcast message, the sensor node adds 1 to the hop count in the data packet, takes the obtained hop count as the hop count from the sensor node to the vehicle, and then continuously broadcasts the hop count from the sensor node to the sensor node in the communication range of the sensor node;
step 1.3, repeating step 1.2 until no new broadcast occurs in the current wireless sensor network;
and if the sensor node receives a plurality of data packets, taking the modified minimum value of the hop count in all the data packets as the hop count from the sensor node to the vehicle.
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